keras tensorflow tutorial

The premise needs to be installed well: ①anaconda3-4.2.0-windows-x86_64 ②pycharm Because the reason for my graphics card is only CPU installed Install the Anaconda is installed in the Python environment, you enter in the cmd there python to see if it shows your Python version informationNow start to install TensorFlow, because in the visit abroad website download is relatively slow, so we want to call Alibaba's imageYou enter%appdata% in the

the loss function (target function) SGD = SGD (l2=0.0,lr=0.05, decay=1e-6, momentum=0.9, nesterov=true) Model.compile ( LosS= ' categorical_crossentropy ', optimizer=sgd,class_mode= "categorical") #调用fit方法, is a training process. The number of epochs trained is set to 10,batch_size of 100. #数据经过随机打乱shuffle =true. Verbose=1, the information that is output during the training process, 0, 1, 23 ways can, does not matter. Show_accuracy=true, each epoch of the training output accuracy. #validation_s

such.tensorflow1.6 or 1.7 with CUDA9.1 is not good, should use 9.0, I was the pit. But fortunately there is a solution, thank you for this article:79433298So I wrote a detailed tutorial on using CUDA9.1 's TensorFlow:79871564Update: TensorFlow package is relatively large, installed more slowly than the ordinary small package, please ensure that the program is ru

Ai This concept seems to suddenly fire up, the beginning of the big score to win Li Shishi Alphago success attracted a lot of attention, but in fact, look at your phone's voice assistant, face recognition on the camera, today's headlines to help you automatically filter out the news, as well as the major music software song "Daily Recommended" ... All kinds of AI have already entered all aspects of our lives. Profoundly affected us, it can be said, this is an AI era.In fact, at the end of last y

). The course content is basically code-based programming, there will be a small amount of deep learning theoretical content. The course starts with some of the most basic knowledge from TensorFlow's most basic diagrams (graphs), sessions (session), tensor (tensor), variables (Variable), and gradually talks about the basics of TensorFlow, And the use of CNN and LSTM in TensorFlow. After the course, we will

powerful influence can lead to the development of a field, as was the case with previous Android systems and Map reduce technologies.Although TensorFlow's official version of the tutorial has been published, but the full English tutorial narrative inevitably make domestic researchers read a little laborious, and personal understanding of the different will cause the inconvenience of use, translated into Ch

environment variable configuration is not directly accessible to the bin and lib\x64 under the package, in the path to add these two paths.Once installed, there will not be more than four environmental variables, and two need to add them themselves. C:\Program Files\nvidia GPU Computing toolkit\cuda\v8.0C:\Program Files\nvidia GPU Computing toolkit\cuda\v8.0\binC:\Program Files\nvidia GPU Computing toolkit\cuda\v8.0\lib\x64C:\Program Files\nvidia GPU Computing TOOLKIT\CUDA\V8.0\LIBNVVP

({x:mnist.test.images, y_: Mnist.test.labels}))The results are as follows:[[email protected] $] python digital_recognition.pyextracting. /train-images-idx3-ubyte.gzextracting. /train-labels-idx1-ubyte.gzextracting. /t10k-images-idx3-ubyte.gzextracting. /t10k-labels-idx1-ubyte.gz0.9039ExplainFlags. Define_string ('data_dir'mnist_data/ ' Directory for storing data')Indicates that we use Mnist_data's top level directory as a storage directory for training data, and if we do not have good training

variable, environment variable, left advanced system settings, properties---Edit text with path editPaste the directory of the Python folder up to the end and add a ";"That is, paste C:\Users\lobsterwww\AppData\Local\Programs\Python\Python36;Click the directory again to see the newly pasted directory is addedExit system settingsstep3 Installation NumPy if not installed, you cannot install TensorFlow directly under PIP. Go to https://pypi.python.org/p

This section corresponds to Google Open source TensorFlow object Detection API Object recognition System Quick start Step (i):Quick Start:jupyter notebook for off-the-shelf inferenceThe steps in this section are simple and do the following:1. After installing Jupyter in the first section, enter the Models folder directory at the Ternimal terminal to execute the command:Jupyter-notebook　　2. The Web page opens Jupyter access to the Object_detection fold

Session:with Tf.device ("/gpu:0"): Session.run (init) coord=tf.train.Coordinator () Threads= Tf.train.start_queue_runners (coord=coord) Max_iter=10000ITER=0ifOs.path.exists (Os.path.join ("Model",'model.ckpt')) isTrue:tf.train.Saver (Max_to_keep=none). Restore (Session, Os.path.join ("Model",'model.ckpt')) whileiterMax_iter:#Loss_np,_,label_np,image_np,inf_np=session.run ([Loss,opti,batch_image,batch_label,inf])B_batch_image,b_batch_label =Session.run ([Batch_image,batch_label]) l

Background: The latest version of Tensoflow has supported Python3.6First, download and install the Anaconda3 built-in Python3.6 version https://www.continuum.io/downloads do not modify its recommended options when installingThen download and install Cuda 8.0 https://developer.nvidia.com/cuda-downloadsThen download and install CUDNN 5.1 (the official recommended version, the latest version is not guaranteed to use) Link: Http://pan.baidu.com/s/1jHK0EFW Password: ai9f add cudnn extracted files to

TensorFlow Official Tutorial: The last layer of the retraining model to cope with the new classification This article mainly includes the following content: TensorFlow Official Tutorial re-training the final layer of the model to cope with the new classification flowers the inception model for the dataset re-training